Gliomas are the most common and aggressive brain tumors, which cause short life expectancy to affected persons. As a result, clinical evaluation is a critical step in improving patients' wellbeing.In this model we design a low-parameter network based on 2D UNet which is used for Brain Tumor Segmentation.The BraTS dataset is used for model training and evaluation.Since the network is a 2D architecture, we need to extract 2D slices from 3D volumes of MRI images. To benefit from 3D contextual information of input images, we extract 2D slices from both Axial and Coronal views, and then train a network for each view separately.Experimental results demonstrate that our method performs favorably against previous models.
Input variables : Brain MRI Image
Output Variables : Brain Tumor Segmentation
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Additional links : ieeexplore.ieee.org
| Model Category | : | Public |
| Date Published | : | January, 2020 |
| Healthcare Domain | : |
Medical Technology
Provider |
| Code | : | github.com |
| Health Risk Management |
| Disease Detection |